Key Associations Found in the Struggle With Sleep in Lung Transplant Recipients
Bibliographic record
Abstract
Introduction Gaps exist in the understanding of the etiology of poor sleep quality after lung transplantation. Research Question: What factors are associated with poor sleep quality in lung transplant recipients? Design A quantitative, single-site, cross-sectional study used an anonymous survey based on 3 scales. The Pittsburgh Sleep Quality Index scale with scores dichotomized to poor versus good sleepers based on the cutoff score > 8. The Hospital Anxiety and Depression Scale evaluated symptoms of anxiety and depression, and the Short Form-12 measured health-related quality of life using the mental and physical component scores. Additional self-reported data included demographic and transplant-related variables. Results The response rate was 38.4% (61/158), and 52.5% of the sample (32/61) evidenced a Pittsburgh Sleep Quality Index score > 8, suggestive of poor sleep quality. Bivariate analyses demonstrated that poor sleep was significantly related to symptoms of depression ( P < .01), anxiety ( P < .01), stressors of hospitalization ( P < .05), and treatment of acute rejection ( P < .05). Multivariate analysis demonstrated that anxiety was significantly associated with poor sleep (odds ratio = 1.34, P < .05). Conclusion Poor subjective sleep quality remains prevalent in lung transplant recipients. Individuals with anxiety symptoms were at a greater risk for poor sleep. Guidance for strategies to improve sleep quality requires further in-depth exploration before implementation of interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".